After the successful training process, the localization and repair models are typically confronted with
new unseen programs with the objective to identify a potential bug and repair it.
This is typically done [1]} by finding the most likely repair for the most likely bug location (according to the model).
However, the most likely repair at the most likely bug location might not always
be meaningful. For example, while the model might be confident that a bug is located at a certain
location, there might not be a suitable repair candidate that can actually fix the bug.
For this reason, we propose an alternative strategy: Instead of taking the most likely repair for the most likely bug location, we search for the most likely meaningful combination of bug location and its repair (and thus ignoring bug localizations that cannot be fixed by the model).
